Decision mechanisms underlying mood-congruent emotional classification.

There is great interest in understanding whether and how mood influences affective processing. Results in the literature have been mixed: some studies show mood-congruent processing but others do not. One limitation of previous work is that decision components for affective processing and responses biases are not dissociated. The present study explored the roles of affective processing and response biases using a drift-diffusion model (DDM) of simple choice. In two experiments, participants decided if words were emotionally positive or negative while listening to music that induced positive or negative mood. The behavioural results showed weak, inconsistent mood-congruency effects. In contrast, the DDM showed consistent effects that were selectively driven by an a-priori bias in response expectation, suggesting that music-induced mood influences expectations about the emotionality of upcoming stimuli, but not the emotionality of the stimuli themselves. Implications for future studies of emotional classification and mood are subsequently discussed.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia.

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner­treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.